Role Overview :

We are looking for a hands-on Data Science Manager to lead a team of 6-8 AI computer vision engineers. This is a player-coach role : you will set technical direction, personally solve the hardest modelling problems, and grow a high-performing engineering team all within the fast-moving environment of a venture-backed industrial AI start-up.

Technical Leadership and Hands-on Delivery :

- Own the end-to-end computer vision roadmap from problem framing and data strategy through model development, edge deployment, and production monitoring.

- Personally architect and build solutions for the most complex vision challenges : novel defect types, extreme class imbalance, multi-camera fusion, and real-time inference on constrained edge hardware.

- Stay at the cutting edge of CV research and rapidly evaluate and adopt new models and techniques (YOLO, SAM, Vision Transformers, Grounding DINO, CLIP) to translate papers into production value.

- Define and enforce engineering standards for the vision stack : model training pipelines, data versioning (DVC), annotation workflows, experiment tracking (W&B, MLflow), and CI/CD for model updates.

- Drive inference optimization, quantization (INT8 / FP16 GPTQ), pruning, and knowledge distillation to meet latency and cost targets across NVIDIA Jetson, industrial PCs, and cloud GPU instances.

Team Building and People Growth :

- Lead, mentor, and grow a team of 6-8 computer vision engineers, set clear goals, run structured code reviews, and create an environment of rapid learning and ownership.

- Hire and onboard strong engineers; raise the technical bar through hands-on pairing and a culture of experimentation over perfection.

- Manage sprint planning, task prioritization, and delivery timelines; balance exploratory R&D with committed product deliverables.

- Act as the primary technical interface between the CV team and cross-functional stakeholders, translating business problems into well-scoped modelling projects.

Innovation and Problem-Solving :

- Identify and frame novel, first-of-its-kind vision problems in industrial settings; design creative solutions combining classical image processing, deep learning, and domain heuristics.

- Champion a data-centric AI approach and invest in annotation quality, active learning, and synthetic data generation.

- Establish robust evaluation frameworks : domain-specific metrics, A/B testing, and systematic failure-mode analysis.

Requirements :

- Bachelor's or master's degree in computer science, AI/ML, electrical engineering, or a related field.

- 4-8 years of hands-on experience in computer vision with a strong track record of taking models from research/prototyping through to production deployment.

- Deep proficiency in Python and PyTorch; strong working knowledge of OpenCV, Albumentations, and image/video processing fundamentals.

- Demonstrated expertise across multiple CV tasks : object detection, segmentation, anomaly detection, pose estimation, or tracking.

- Hands-on experience with modern model families (YOLO, RT-DETR, SAM, CNN backbones).

- Production experience deploying models to edge or on-prem hardware using TensorRT, ONNX Runtime, or OpenVINO; comfort with Docker, Kubernetes, and at least one cloud platform (AWS / Azure / GCP).

- Experience in a high-growth start-up or similarly fast-paced environment.

- Strong first-principles problem-solving ability and excellent communication skills.

Good to Have :

- Prior experience leading or mentoring a small engineering team.

- Experience with industrial or manufacturing domains and integration with PLCs/SCADA systems.

- Familiarity with zero-shot/open-vocabulary detection (Grounding DINO, YOLO-World, CLIP) and foundation models.

- Exposure to vision-language models (GPT-4o vision, LLaVA).

- Knowledge of 3D vision, depth estimation, or multi-camera calibration.

- Experience with multi-object tracking (ByteTrack, BoT-SORT) and video analytics pipelines.

- Contributions to open-source CV projects, publications, or strong Kaggle competition results.